Abstract
The R package sensobol provides several functions to conduct variance-based uncertainty and sensitivity analysis, from the estimation of sensitivity indices to the visual representation of the results. It implements several state-of-the-art first and total-order estimators and allows the computation of up to fourth-order effects, as well as of the approximation error, in a swift and user-friendly way. Its flexibility makes it also appropriate for models with either a scalar or a multivariate output. We illustrate its functionality by conducting a variance-based sensitivity analysis of three classic models: the Sobol’ (1998) G function, the logistic population growth model of Verhulst (1845), and the spruce bud-worm and forest model of Ludwig, Jones, and Holling (1976).
Original language | English |
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Pages (from-to) | 1-37 |
Number of pages | 37 |
Journal | Journal of Statistical Software |
Volume | 102 |
Issue number | 5 |
DOIs | |
Publication status | Published - 30 Apr 2022 |
Bibliographical note
Funding Information:This work has been funded by the European Commission (Marie Skłodowska-Curie Global Fellowship, grant number 792178 to AP).
Publisher Copyright:
© 2022, American Statistical Association. All rights reserved.
Keywords
- uncertainty
- sensitivity
- modeling
- ecology
ASJC Scopus subject areas
- Software
- Statistics and Probability
- Statistics, Probability and Uncertainty